The field of computer vision is rapidly advancing, with significant developments in pose estimation and 3D reconstruction. Researchers are exploring innovative approaches to estimate the 6D pose of objects and humans, leveraging reference geometric correspondences, zero-shot learning, and stereo vision. These methods are enabling more accurate and robust pose estimation, even in the presence of occlusions and varying lighting conditions. Furthermore, the development of new frameworks and benchmarks, such as PoseBench3D, is facilitating the evaluation and comparison of different pose estimation methods. Notable papers in this area include RefPose, which achieves state-of-the-art results in 6D pose estimation, and SurgPose, which pioneers the application of RGB-D zero-shot methods in Robot-assisted Minimally Invasive Surgery. Additionally, researchers are making progress in 3D reconstruction, with advances in structure from motion methods and the development of new taxation systems. The field is also seeing increased focus on applications such as virtual reality, skin cancer screening, and animal behavior analysis.